UWB-Fat: Non-Intrusive Body Fat Measurement Using Commodity Ultra-Wideband Radar

TL;DR

UWB-Fat is a novel system that uses commodity ultra-wideband radar to non-intrusively and accurately estimate body fat and skinfold thickness, providing an accessible alternative to traditional methods.

Contribution

It introduces the first UWB radar-based system for non-intrusive, caliper-equivalent skinfold thickness estimation, combining physics-inspired modeling with signal analysis.

Findings

Achieved a root mean square error of 0.63 mm in skinfold thickness estimation.

Demonstrated potential for low-cost, self-administered body fat monitoring.

Validated on 15 participants with promising accuracy.

Abstract

Body fat percentage and its spatial distribution are clinically important health indicators. However, existing measurement methods often impose a tradeoff between accuracy and accessibility. Clinical-grade techniques, such as Dual-Energy X-ray Absorptiometry (DEXA) and hydrostatic weighing, provide accurate measurements but require specialized equipment and trained operators, making them difficult to access and unsuitable for everyday use. In contrast, consumer-level methods, such as Bioelectrical Impedance Analysis (BIA) smart scales and skinfold calipers, are more accessible but typically provide only coarse-grained estimates, are prone to user error, or require intrusive physical contact. In this work, we present UWB-Fat, the first system that leverages commodity ultra-wideband (UWB) radar to enable non-intrusive, accessible, and accurate caliper-equivalent skinfold thickness estimation, serving as a convenient replacement for the skinfold caliper. UWB-Fat collects UWB signal at specified body sites non-intrusively without operator assistance. It extracts body-composition-related features from UWB signals by exploiting dielectric contrasts among skin, fat, and muscle tissues. Then, it uses a physics-inspired model to estimate site-specific skinfold thickness. We evaluate UWB-Fat on 15 participants, achieving a root mean square error of 0.63~mm for pooled-site subcutaneous fat thickness. These results highlight the potential of UWB-Fat to support low-cost, self-administered, and everyday body fat monitoring.